{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"machine_hack.ipynb","provenance":[],"toc_visible":true,"mount_file_id":"1WLtYieLysSMkrQKkU0_q0n04xfB1LEUc","authorship_tag":"ABX9TyOBd0I785sDBSpAFcWb5dL+"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"id":"NRXbS5UeI8Hi","executionInfo":{"status":"ok","timestamp":1603532263901,"user_tz":-330,"elapsed":14185,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"cd8d9586-dfac-4b1e-c7dd-ac5a56af59f8","colab":{"base_uri":"https://localhost:8080/","height":343}},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline\n","plt.style.use('seaborn-whitegrid')\n","\n","from sklearn.model_selection import train_test_split, StratifiedKFold\n","from sklearn.metrics import accuracy_score, f1_score\n","from sklearn.preprocessing import StandardScaler\n","\n","!pip install catboost\n","from lightgbm import LGBMRegressor\n","from catboost import CatBoostRegressor\n","from xgboost import XGBRegressor\n","\n","import warnings\n","warnings.simplefilter('ignore')\n","\n","from IPython.core.interactiveshell import InteractiveShell\n","InteractiveShell.ast_node_interactivity = \"all\"\n"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting catboost\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/39/128fff65072c8327371e3c594f3c826d29c85b21cb6485980353b168e0e4/catboost-0.24.2-cp36-none-manylinux1_x86_64.whl (66.1MB)\n","\u001b[K     |████████████████████████████████| 66.2MB 62kB/s \n","\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n","Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.2)\n","Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n","Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n","Installing collected packages: catboost\n","Successfully installed catboost-0.24.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YkhcvoPHDUt1","executionInfo":{"status":"ok","timestamp":1603532287452,"user_tz":-330,"elapsed":1366,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["from sklearn.model_selection import RandomizedSearchCV"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"WdgrgKUiLPge","executionInfo":{"status":"ok","timestamp":1603532291720,"user_tz":-330,"elapsed":4391,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["train = pd.read_csv(\"/content/drive/My Drive/Machine_Hack/House Price Prediction Challenge/Train.csv\")\n","test = pd.read_csv(\"/content/drive/My Drive/Machine_Hack/House Price Prediction Challenge/Test.csv\")\n","ss = pd.read_csv(\"/content/drive/My Drive/Machine_Hack/House Price Prediction Challenge/sample_submission.csv\")"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"biQ7OQJOLnZJ","executionInfo":{"status":"ok","timestamp":1603532292353,"user_tz":-330,"elapsed":1480,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"0fc06086-7d62-441e-d4dd-cc1d81b16475","colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["ss.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   TARGET(PRICE_IN_LACS)\n","0                    0.0\n","1                    0.0\n","2                    0.0\n","3                    0.0\n","4                    0.0"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"o1qr_kJILtQ-","executionInfo":{"status":"ok","timestamp":1603532294288,"user_tz":-330,"elapsed":1469,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"e7136383-9f64-4b44-df3d-4a50dc1c8c29","colab":{"base_uri":"https://localhost:8080/","height":259}},"source":["train.head(2)\n","test.head(2)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Owner</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>BHK</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Dealer</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>BHK</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  POSTED_BY  UNDER_CONSTRUCTION  ...   LATITUDE  TARGET(PRICE_IN_LACS)\n","0     Owner                   0  ...  77.597960                   55.0\n","1    Dealer                   0  ...  76.644605                   51.0\n","\n","[2 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Owner</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>BHK</td>\n","      <td>545.17134</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Kamrej,Surat</td>\n","      <td>21.262000</td>\n","      <td>73.047700</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Dealer</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>BHK</td>\n","      <td>800.00000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Panvel,Lalitpur</td>\n","      <td>18.966114</td>\n","      <td>73.148278</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  POSTED_BY  UNDER_CONSTRUCTION  RERA  ...          ADDRESS  LONGITUDE   LATITUDE\n","0     Owner                   0     0  ...     Kamrej,Surat  21.262000  73.047700\n","1    Dealer                   1     1  ...  Panvel,Lalitpur  18.966114  73.148278\n","\n","[2 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"c-mcZbxjNnLq","executionInfo":{"status":"ok","timestamp":1603532297306,"user_tz":-330,"elapsed":1221,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["TARGET_COL = 'TARGET(PRICE_IN_LACS)'\n","num_col = ['SQUARE_FT', 'LONGITUDE', 'LATITUDE']\n","cat_col = ['POSTED_BY', 'BHK_OR_RK']\n","num_cat_col = ['UNDER_CONSTRUCTION', 'RERA', 'BHK_NO.', 'READY_TO_MOVE', 'RESALE']\n","text_col = ['ADDRESS']"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"b8kG65pWK4_y","executionInfo":{"status":"ok","timestamp":1603532299725,"user_tz":-330,"elapsed":1355,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"656ba519-b10a-478d-afb4-5c816e8640bd","colab":{"base_uri":"https://localhost:8080/","height":162}},"source":["train[train[TARGET_COL]>20000]\n","# 10745, 11238, 15724"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10745</th>\n","      <td>Owner</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>BHK</td>\n","      <td>2.545455e+08</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Malur,Bangalore</td>\n","      <td>13.021000</td>\n","      <td>77.938000</td>\n","      <td>28000.0</td>\n","    </tr>\n","    <tr>\n","      <th>11238</th>\n","      <td>Dealer</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>BHK</td>\n","      <td>1.875000e+08</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>R.T. Nagar,Bangalore</td>\n","      <td>13.018900</td>\n","      <td>77.596300</td>\n","      <td>30000.0</td>\n","    </tr>\n","    <tr>\n","      <th>15724</th>\n","      <td>Owner</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>BHK</td>\n","      <td>8.064516e+07</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Lakkasandra,Bangalore</td>\n","      <td>12.795926</td>\n","      <td>77.331535</td>\n","      <td>25000.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      POSTED_BY  UNDER_CONSTRUCTION  ...   LATITUDE  TARGET(PRICE_IN_LACS)\n","10745     Owner                   0  ...  77.938000                28000.0\n","11238    Dealer                   0  ...  77.596300                30000.0\n","15724     Owner                   0  ...  77.331535                25000.0\n","\n","[3 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"2fyBAZWGLlN2","executionInfo":{"status":"ok","timestamp":1603532307408,"user_tz":-330,"elapsed":1508,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"e91e22f1-d793-4787-995c-410067fa9cdc","colab":{"base_uri":"https://localhost:8080/","height":69}},"source":["a = train.index[10745]\n","b = train.index[11238]\n","c = train.index[15724]\n","train = train.drop([a,b,c])\n","train[train[TARGET_COL]>20000]"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [POSTED_BY, UNDER_CONSTRUCTION, RERA, BHK_NO., BHK_OR_RK, SQUARE_FT, READY_TO_MOVE, RESALE, ADDRESS, LONGITUDE, LATITUDE, TARGET(PRICE_IN_LACS)]\n","Index: []"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"rcR7NIwQNouY","executionInfo":{"status":"ok","timestamp":1603537894027,"user_tz":-330,"elapsed":1124,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["df = pd.concat([train,test], axis=0).reset_index(drop=True)"],"execution_count":164,"outputs":[]},{"cell_type":"code","metadata":{"id":"zilGlMz_OJWH","executionInfo":{"status":"ok","timestamp":1603537895567,"user_tz":-330,"elapsed":1642,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["df[cat_col] = df[cat_col].apply(lambda x: pd.factorize(x)[0])"],"execution_count":165,"outputs":[]},{"cell_type":"code","metadata":{"id":"2BThulk5QMwY","executionInfo":{"status":"ok","timestamp":1603537896107,"user_tz":-330,"elapsed":1204,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"a3645dad-4094-4d7c-90e2-b16a7df4b13c","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["df = df.fillna(-999)\n","df.isnull().sum().sum()"],"execution_count":166,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{"tags":[]},"execution_count":166}]},{"cell_type":"code","metadata":{"id":"YKNKQ94ageHP","executionInfo":{"status":"ok","timestamp":1603537897375,"user_tz":-330,"elapsed":1415,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"2145773a-4c96-4650-ef27-1d86d444c2d6","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train.shape, test.shape"],"execution_count":167,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((29448, 12), (68720, 11))"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"markdown","metadata":{"id":"KCVGD5PmnqRk"},"source":["# Features Engineering"]},{"cell_type":"code","metadata":{"id":"1ax8ybjbKSCg","executionInfo":{"status":"ok","timestamp":1603537899548,"user_tz":-330,"elapsed":1165,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["new_df = pd.DataFrame()\n","new_df['address_count'] = df['ADDRESS'].apply(lambda x: len(x))\n","df['address_count'] = new_df['address_count']"],"execution_count":168,"outputs":[]},{"cell_type":"code","metadata":{"id":"kwDQMr-Rnwtc","executionInfo":{"status":"ok","timestamp":1603537900298,"user_tz":-330,"elapsed":886,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["new_df['address_ct'] = new_df['address_count'].map(new_df['address_count'].value_counts())\n","\n","df['address_ct'] = new_df['address_ct']\n"],"execution_count":169,"outputs":[]},{"cell_type":"code","metadata":{"id":"r2GNKPG1nFD0","executionInfo":{"status":"ok","timestamp":1603537207645,"user_tz":-330,"elapsed":1638,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["#df['add_group_posted'] = df.groupby('address_count')['RERA'].transform('nunique')"],"execution_count":101,"outputs":[]},{"cell_type":"code","metadata":{"id":"khtvRcUhoBpQ","executionInfo":{"status":"ok","timestamp":1603537904462,"user_tz":-330,"elapsed":1261,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"3c8ebebe-cc7c-4831-93fa-561109798e4c","colab":{"base_uri":"https://localhost:8080/","height":221}},"source":["df['add_group_RESALE'] = df.groupby('ADDRESS')['RESALE'].transform('nunique')\n","df['add_group_RESALE']"],"execution_count":170,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0        1\n","1        1\n","2        1\n","3        1\n","4        2\n","        ..\n","98163    2\n","98164    2\n","98165    2\n","98166    2\n","98167    2\n","Name: add_group_RESALE, Length: 98168, dtype: int64"]},"metadata":{"tags":[]},"execution_count":170}]},{"cell_type":"code","metadata":{"id":"TURQN6c5pSoN","executionInfo":{"status":"ok","timestamp":1603537907389,"user_tz":-330,"elapsed":1285,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"5c13618a-d2ba-476d-ddef-8d21b79c10ae","colab":{"base_uri":"https://localhost:8080/","height":196}},"source":["df.head(3)"],"execution_count":171,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","      <td>21</td>\n","      <td>9672</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","      <td>25</td>\n","      <td>4186</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>933.159722</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Jigani,Bangalore</td>\n","      <td>12.778033</td>\n","      <td>77.632191</td>\n","      <td>43.0</td>\n","      <td>16</td>\n","      <td>6212</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   POSTED_BY  UNDER_CONSTRUCTION  ...  address_ct  add_group_RESALE\n","0          0                   0  ...        9672                 1\n","1          1                   0  ...        4186                 1\n","2          0                   0  ...        6212                 1\n","\n","[3 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":171}]},{"cell_type":"code","metadata":{"id":"kxLB5D0Jpq27","executionInfo":{"status":"ok","timestamp":1603537908893,"user_tz":-330,"elapsed":1837,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["new_add = pd.concat([train['ADDRESS'], test['ADDRESS']], axis=0)\n","adr = new_add.str.split(',', n = 1, expand = True)\n","df['adr_1'] = list(adr[0])\n","df['adr_2'] = list(adr[1])"],"execution_count":172,"outputs":[]},{"cell_type":"code","metadata":{"id":"8Ekshh-AqStG","executionInfo":{"status":"ok","timestamp":1603537911363,"user_tz":-330,"elapsed":1893,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["addr = ['adr_2', 'adr_1']"],"execution_count":173,"outputs":[]},{"cell_type":"code","metadata":{"id":"TJ0rkLO7qM7O","executionInfo":{"status":"ok","timestamp":1603537912141,"user_tz":-330,"elapsed":1836,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["df[addr] = df[addr].apply(lambda x: pd.factorize(x)[0])"],"execution_count":174,"outputs":[]},{"cell_type":"code","metadata":{"id":"GDhjDEtXqBsS","executionInfo":{"status":"ok","timestamp":1603537912737,"user_tz":-330,"elapsed":1481,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"51ad3eb3-0dd7-47dd-a6af-8fbd1b10d0a6","colab":{"base_uri":"https://localhost:8080/","height":165}},"source":["df.head(2)"],"execution_count":175,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","      <th>adr_1</th>\n","      <th>adr_2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","      <td>21</td>\n","      <td>9672</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","      <td>25</td>\n","      <td>4186</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   POSTED_BY  UNDER_CONSTRUCTION  RERA  ...  add_group_RESALE  adr_1  adr_2\n","0          0                   0     0  ...                 1      0      0\n","1          1                   0     0  ...                 1      1      1\n","\n","[2 rows x 17 columns]"]},"metadata":{"tags":[]},"execution_count":175}]},{"cell_type":"code","metadata":{"id":"24RnD52eprAW","executionInfo":{"status":"ok","timestamp":1603537914967,"user_tz":-330,"elapsed":1237,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["df['SQUARE_FT/BHK_NO.'] = df['SQUARE_FT']//df['BHK_NO.']"],"execution_count":176,"outputs":[]},{"cell_type":"code","metadata":{"id":"P6MYfZ4TpqoR"},"source":["#df['Under_const_resale'] = df['UNDER_CONSTRUCTION']*df['RESALE']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X4Xp4Az4OFFy"},"source":["f_name[f_name['import']==0]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_OfgFo5ApmQg","executionInfo":{"status":"ok","timestamp":1603537916903,"user_tz":-330,"elapsed":1552,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"38357807-5072-4ea9-af01-99a45e151d14","colab":{"base_uri":"https://localhost:8080/","height":196}},"source":["temp = df.groupby(['adr_1']).agg({'adr_1':['sum','min','max'],\n","                                           'POSTED_BY':['count','sum','min','max','mean'],\n","                                           'UNDER_CONSTRUCTION':['count','sum','mean'],\n","                                           'RERA':['sum','min','mean'],\n","                                           'BHK_NO.':['min','max','mean'],\n","                                           'BHK_OR_RK':['mean'],\n","                                           'SQUARE_FT':['sum','min','max','mean'],\n","                                           'RESALE':['sum','min'],\n","                                           'SQUARE_FT/BHK_NO.': ['sum','min','max','mean']})\n","\n","temp.columns = ['_'.join(x) for x in temp.columns]\n","new_df = pd.merge(df,temp,on=['adr_1'],how='left')\n","#temp.shape\n","new_df.head(3)"],"execution_count":177,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","      <th>adr_1</th>\n","      <th>adr_2</th>\n","      <th>SQUARE_FT/BHK_NO.</th>\n","      <th>adr_1_sum</th>\n","      <th>adr_1_min</th>\n","      <th>adr_1_max</th>\n","      <th>POSTED_BY_count</th>\n","      <th>POSTED_BY_sum</th>\n","      <th>POSTED_BY_min</th>\n","      <th>POSTED_BY_max</th>\n","      <th>POSTED_BY_mean</th>\n","      <th>UNDER_CONSTRUCTION_count</th>\n","      <th>UNDER_CONSTRUCTION_sum</th>\n","      <th>UNDER_CONSTRUCTION_mean</th>\n","      <th>RERA_sum</th>\n","      <th>RERA_min</th>\n","      <th>RERA_mean</th>\n","      <th>BHK_NO._min</th>\n","      <th>BHK_NO._max</th>\n","      <th>BHK_NO._mean</th>\n","      <th>BHK_OR_RK_mean</th>\n","      <th>SQUARE_FT_sum</th>\n","      <th>SQUARE_FT_min</th>\n","      <th>SQUARE_FT_max</th>\n","      <th>SQUARE_FT_mean</th>\n","      <th>RESALE_sum</th>\n","      <th>RESALE_min</th>\n","      <th>SQUARE_FT/BHK_NO._sum</th>\n","      <th>SQUARE_FT/BHK_NO._min</th>\n","      <th>SQUARE_FT/BHK_NO._max</th>\n","      <th>SQUARE_FT/BHK_NO._mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","      <td>21</td>\n","      <td>9672</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>650.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>2.500000</td>\n","      <td>0.0</td>\n","      <td>1.300130e+07</td>\n","      <td>1300.236407</td>\n","      <td>1.300000e+07</td>\n","      <td>6.500650e+06</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>4333983.0</td>\n","      <td>650.0</td>\n","      <td>4333333.0</td>\n","      <td>2.166992e+06</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","      <td>25</td>\n","      <td>4186</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>637.0</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.722222</td>\n","      <td>18</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.055556</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>2.666667</td>\n","      <td>0.0</td>\n","      <td>2.649047e+04</td>\n","      <td>885.156605</td>\n","      <td>2.438529e+03</td>\n","      <td>1.471693e+03</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>10087.0</td>\n","      <td>386.0</td>\n","      <td>681.0</td>\n","      <td>5.603889e+02</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>933.159722</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Jigani,Bangalore</td>\n","      <td>12.778033</td>\n","      <td>77.632191</td>\n","      <td>43.0</td>\n","      <td>16</td>\n","      <td>6212</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>466.0</td>\n","      <td>82</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>41</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.073171</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>2.292683</td>\n","      <td>0.0</td>\n","      <td>4.289350e+04</td>\n","      <td>555.041628</td>\n","      <td>1.536127e+03</td>\n","      <td>1.046183e+03</td>\n","      <td>41</td>\n","      <td>1</td>\n","      <td>19041.0</td>\n","      <td>360.0</td>\n","      <td>600.0</td>\n","      <td>4.644146e+02</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   POSTED_BY  UNDER_CONSTRUCTION  ...  SQUARE_FT/BHK_NO._max  SQUARE_FT/BHK_NO._mean\n","0          0                   0  ...              4333333.0            2.166992e+06\n","1          1                   0  ...                  681.0            5.603889e+02\n","2          0                   0  ...                  600.0            4.644146e+02\n","\n","[3 rows x 46 columns]"]},"metadata":{"tags":[]},"execution_count":177}]},{"cell_type":"code","metadata":{"id":"Wrkfeu50HIf3","executionInfo":{"status":"ok","timestamp":1603537920517,"user_tz":-330,"elapsed":1113,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"81d738fb-bc29-4d2a-d916-48cd5aecf908","colab":{"base_uri":"https://localhost:8080/","height":80}},"source":["f_name[f_name['import']==1]"],"execution_count":178,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>col</th>\n","      <th>import</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>6</th>\n","      <td>READY_TO_MOVE</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             col  import\n","6  READY_TO_MOVE       1"]},"metadata":{"tags":[]},"execution_count":178}]},{"cell_type":"code","metadata":{"id":"A0iT92r4wQZ0","executionInfo":{"status":"ok","timestamp":1603537921870,"user_tz":-330,"elapsed":1444,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"ea48d524-9e93-47c7-bb78-bb88e9322002","colab":{"base_uri":"https://localhost:8080/","height":196}},"source":["temp1 = df.groupby(['adr_2']).agg({'adr_1':['sum','min','max'],\n","                                           'adr_2':['sum','min'],\n","                                           'POSTED_BY':['count','sum','max','mean'],\n","                                           'UNDER_CONSTRUCTION':['count','sum','mean'],\n","                                           'RERA':['sum','mean'],\n","                                           'SQUARE_FT':['sum','min','mean'],\n","                                           'RESALE':['sum','mean'],\n","                                           'SQUARE_FT/BHK_NO.': ['sum','min','max','mean']\n","                                   })\n","\n","temp1.columns = ['_'.join(x) for x in temp1.columns]\n","new_df = pd.merge(new_df,temp1,on=['adr_2'],how='left')\n","#temp.shape\n","new_df.head(3)"],"execution_count":179,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","      <th>adr_1</th>\n","      <th>adr_2</th>\n","      <th>SQUARE_FT/BHK_NO.</th>\n","      <th>adr_1_sum_x</th>\n","      <th>adr_1_min_x</th>\n","      <th>adr_1_max_x</th>\n","      <th>POSTED_BY_count_x</th>\n","      <th>POSTED_BY_sum_x</th>\n","      <th>POSTED_BY_min</th>\n","      <th>POSTED_BY_max_x</th>\n","      <th>POSTED_BY_mean_x</th>\n","      <th>UNDER_CONSTRUCTION_count_x</th>\n","      <th>UNDER_CONSTRUCTION_sum_x</th>\n","      <th>UNDER_CONSTRUCTION_mean_x</th>\n","      <th>RERA_sum_x</th>\n","      <th>RERA_min</th>\n","      <th>RERA_mean_x</th>\n","      <th>BHK_NO._min</th>\n","      <th>BHK_NO._max</th>\n","      <th>BHK_NO._mean</th>\n","      <th>BHK_OR_RK_mean</th>\n","      <th>SQUARE_FT_sum_x</th>\n","      <th>SQUARE_FT_min_x</th>\n","      <th>SQUARE_FT_max</th>\n","      <th>SQUARE_FT_mean_x</th>\n","      <th>RESALE_sum_x</th>\n","      <th>RESALE_min</th>\n","      <th>SQUARE_FT/BHK_NO._sum_x</th>\n","      <th>SQUARE_FT/BHK_NO._min_x</th>\n","      <th>SQUARE_FT/BHK_NO._max_x</th>\n","      <th>SQUARE_FT/BHK_NO._mean_x</th>\n","      <th>adr_1_sum_y</th>\n","      <th>adr_1_min_y</th>\n","      <th>adr_1_max_y</th>\n","      <th>adr_2_sum</th>\n","      <th>adr_2_min</th>\n","      <th>POSTED_BY_count_y</th>\n","      <th>POSTED_BY_sum_y</th>\n","      <th>POSTED_BY_max_y</th>\n","      <th>POSTED_BY_mean_y</th>\n","      <th>UNDER_CONSTRUCTION_count_y</th>\n","      <th>UNDER_CONSTRUCTION_sum_y</th>\n","      <th>UNDER_CONSTRUCTION_mean_y</th>\n","      <th>RERA_sum_y</th>\n","      <th>RERA_mean_y</th>\n","      <th>SQUARE_FT_sum_y</th>\n","      <th>SQUARE_FT_min_y</th>\n","      <th>SQUARE_FT_mean_y</th>\n","      <th>RESALE_sum_y</th>\n","      <th>RESALE_mean</th>\n","      <th>SQUARE_FT/BHK_NO._sum_y</th>\n","      <th>SQUARE_FT/BHK_NO._min_y</th>\n","      <th>SQUARE_FT/BHK_NO._max_y</th>\n","      <th>SQUARE_FT/BHK_NO._mean_y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","      <td>21</td>\n","      <td>9672</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>650.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>2.500000</td>\n","      <td>0.0</td>\n","      <td>1.300130e+07</td>\n","      <td>1300.236407</td>\n","      <td>1.300000e+07</td>\n","      <td>6.500650e+06</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>4333983.0</td>\n","      <td>650.0</td>\n","      <td>4333333.0</td>\n","      <td>2.166992e+06</td>\n","      <td>21577265</td>\n","      <td>0</td>\n","      <td>11039</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>14260</td>\n","      <td>8063</td>\n","      <td>2</td>\n","      <td>0.565428</td>\n","      <td>14260</td>\n","      <td>1725</td>\n","      <td>0.120968</td>\n","      <td>3794</td>\n","      <td>0.266059</td>\n","      <td>8.633606e+07</td>\n","      <td>30.000035</td>\n","      <td>6054.422061</td>\n","      <td>13710</td>\n","      <td>0.961431</td>\n","      <td>55278406.0</td>\n","      <td>10.0</td>\n","      <td>40163934.0</td>\n","      <td>3876.466059</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","      <td>25</td>\n","      <td>4186</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>637.0</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.722222</td>\n","      <td>18</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.055556</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>2.666667</td>\n","      <td>0.0</td>\n","      <td>2.649047e+04</td>\n","      <td>885.156605</td>\n","      <td>2.438529e+03</td>\n","      <td>1.471693e+03</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>10087.0</td>\n","      <td>386.0</td>\n","      <td>681.0</td>\n","      <td>5.603889e+02</td>\n","      <td>548347</td>\n","      <td>1</td>\n","      <td>10742</td>\n","      <td>183</td>\n","      <td>1</td>\n","      <td>183</td>\n","      <td>94</td>\n","      <td>2</td>\n","      <td>0.513661</td>\n","      <td>183</td>\n","      <td>11</td>\n","      <td>0.060109</td>\n","      <td>23</td>\n","      <td>0.125683</td>\n","      <td>2.504359e+05</td>\n","      <td>60.000375</td>\n","      <td>1368.502102</td>\n","      <td>175</td>\n","      <td>0.956284</td>\n","      <td>103377.0</td>\n","      <td>12.0</td>\n","      <td>995.0</td>\n","      <td>564.901639</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>933.159722</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Jigani,Bangalore</td>\n","      <td>12.778033</td>\n","      <td>77.632191</td>\n","      <td>43.0</td>\n","      <td>16</td>\n","      <td>6212</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>466.0</td>\n","      <td>82</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>41</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.073171</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>2.292683</td>\n","      <td>0.0</td>\n","      <td>4.289350e+04</td>\n","      <td>555.041628</td>\n","      <td>1.536127e+03</td>\n","      <td>1.046183e+03</td>\n","      <td>41</td>\n","      <td>1</td>\n","      <td>19041.0</td>\n","      <td>360.0</td>\n","      <td>600.0</td>\n","      <td>4.644146e+02</td>\n","      <td>21577265</td>\n","      <td>0</td>\n","      <td>11039</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>14260</td>\n","      <td>8063</td>\n","      <td>2</td>\n","      <td>0.565428</td>\n","      <td>14260</td>\n","      <td>1725</td>\n","      <td>0.120968</td>\n","      <td>3794</td>\n","      <td>0.266059</td>\n","      <td>8.633606e+07</td>\n","      <td>30.000035</td>\n","      <td>6054.422061</td>\n","      <td>13710</td>\n","      <td>0.961431</td>\n","      <td>55278406.0</td>\n","      <td>10.0</td>\n","      <td>40163934.0</td>\n","      <td>3876.466059</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   POSTED_BY  ...  SQUARE_FT/BHK_NO._mean_y\n","0          0  ...               3876.466059\n","1          1  ...                564.901639\n","2          0  ...               3876.466059\n","\n","[3 rows x 69 columns]"]},"metadata":{"tags":[]},"execution_count":179}]},{"cell_type":"code","metadata":{"id":"9bZCJ86TNJ8x","executionInfo":{"status":"ok","timestamp":1603537925552,"user_tz":-330,"elapsed":1463,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"abecd24a-0da8-499a-9f94-23da6a4b0d56","colab":{"base_uri":"https://localhost:8080/","height":80}},"source":["f_name[f_name['import']==1]"],"execution_count":180,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>col</th>\n","      <th>import</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>6</th>\n","      <td>READY_TO_MOVE</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             col  import\n","6  READY_TO_MOVE       1"]},"metadata":{"tags":[]},"execution_count":180}]},{"cell_type":"code","metadata":{"id":"Wn6G-mmzp_tY","executionInfo":{"status":"ok","timestamp":1603537928130,"user_tz":-330,"elapsed":1747,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"730ac8ee-33cf-4b80-d205-9f186e49dd41","colab":{"base_uri":"https://localhost:8080/","height":225}},"source":["temp2 = df.groupby(['SQUARE_FT']).agg({'POSTED_BY':['count','sum','min','max','mean'],\n","                                        'RERA':['count','sum','min','mean'],\n","                                        'BHK_NO.':['sum','max','mean'],\n","                                        'SQUARE_FT':['sum','min'],\n","                                        'RESALE':['sum','min','mean'],\n","                                        'SQUARE_FT/BHK_NO.': ['sum','min','max','mean']\n","                                   })\n","\n","temp2.columns = ['_'.join(z) for z in temp2.columns]\n","new_df = pd.merge(new_df,temp2,on=['SQUARE_FT'],how='left')\n","#temp.shape\n","new_df.head(3)"],"execution_count":181,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>ADDRESS</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","      <th>adr_1</th>\n","      <th>adr_2</th>\n","      <th>SQUARE_FT/BHK_NO.</th>\n","      <th>adr_1_sum_x</th>\n","      <th>adr_1_min_x</th>\n","      <th>adr_1_max_x</th>\n","      <th>POSTED_BY_count_x</th>\n","      <th>POSTED_BY_sum_x</th>\n","      <th>POSTED_BY_min_x</th>\n","      <th>POSTED_BY_max_x</th>\n","      <th>POSTED_BY_mean_x</th>\n","      <th>UNDER_CONSTRUCTION_count_x</th>\n","      <th>UNDER_CONSTRUCTION_sum_x</th>\n","      <th>UNDER_CONSTRUCTION_mean_x</th>\n","      <th>RERA_sum_x</th>\n","      <th>RERA_min_x</th>\n","      <th>RERA_mean_x</th>\n","      <th>BHK_NO._min</th>\n","      <th>BHK_NO._max_x</th>\n","      <th>BHK_NO._mean_x</th>\n","      <th>BHK_OR_RK_mean</th>\n","      <th>SQUARE_FT_sum_x</th>\n","      <th>SQUARE_FT_min_x</th>\n","      <th>SQUARE_FT_max</th>\n","      <th>SQUARE_FT_mean_x</th>\n","      <th>...</th>\n","      <th>adr_2_min</th>\n","      <th>POSTED_BY_count_y</th>\n","      <th>POSTED_BY_sum_y</th>\n","      <th>POSTED_BY_max_y</th>\n","      <th>POSTED_BY_mean_y</th>\n","      <th>UNDER_CONSTRUCTION_count_y</th>\n","      <th>UNDER_CONSTRUCTION_sum_y</th>\n","      <th>UNDER_CONSTRUCTION_mean_y</th>\n","      <th>RERA_sum_y</th>\n","      <th>RERA_mean_y</th>\n","      <th>SQUARE_FT_sum_y</th>\n","      <th>SQUARE_FT_min_y</th>\n","      <th>SQUARE_FT_mean_y</th>\n","      <th>RESALE_sum_y</th>\n","      <th>RESALE_mean_x</th>\n","      <th>SQUARE_FT/BHK_NO._sum_y</th>\n","      <th>SQUARE_FT/BHK_NO._min_y</th>\n","      <th>SQUARE_FT/BHK_NO._max_y</th>\n","      <th>SQUARE_FT/BHK_NO._mean_y</th>\n","      <th>POSTED_BY_count</th>\n","      <th>POSTED_BY_sum</th>\n","      <th>POSTED_BY_min_y</th>\n","      <th>POSTED_BY_max</th>\n","      <th>POSTED_BY_mean</th>\n","      <th>RERA_count</th>\n","      <th>RERA_sum</th>\n","      <th>RERA_min_y</th>\n","      <th>RERA_mean</th>\n","      <th>BHK_NO._sum</th>\n","      <th>BHK_NO._max_y</th>\n","      <th>BHK_NO._mean_y</th>\n","      <th>SQUARE_FT_sum</th>\n","      <th>SQUARE_FT_min</th>\n","      <th>RESALE_sum</th>\n","      <th>RESALE_min_y</th>\n","      <th>RESALE_mean_y</th>\n","      <th>SQUARE_FT/BHK_NO._sum</th>\n","      <th>SQUARE_FT/BHK_NO._min</th>\n","      <th>SQUARE_FT/BHK_NO._max</th>\n","      <th>SQUARE_FT/BHK_NO._mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1300.236407</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ksfc Layout,Bangalore</td>\n","      <td>12.969910</td>\n","      <td>77.597960</td>\n","      <td>55.0</td>\n","      <td>21</td>\n","      <td>9672</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>650.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>2.500000</td>\n","      <td>0.0</td>\n","      <td>1.300130e+07</td>\n","      <td>1300.236407</td>\n","      <td>1.300000e+07</td>\n","      <td>6.500650e+06</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>14260</td>\n","      <td>8063</td>\n","      <td>2</td>\n","      <td>0.565428</td>\n","      <td>14260</td>\n","      <td>1725</td>\n","      <td>0.120968</td>\n","      <td>3794</td>\n","      <td>0.266059</td>\n","      <td>8.633606e+07</td>\n","      <td>30.000035</td>\n","      <td>6054.422061</td>\n","      <td>13710</td>\n","      <td>0.961431</td>\n","      <td>55278406.0</td>\n","      <td>10.0</td>\n","      <td>40163934.0</td>\n","      <td>3876.466059</td>\n","      <td>51</td>\n","      <td>11</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.215686</td>\n","      <td>51</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.058824</td>\n","      <td>129</td>\n","      <td>4</td>\n","      <td>2.529412</td>\n","      <td>66312.056757</td>\n","      <td>1300.236407</td>\n","      <td>51</td>\n","      <td>1</td>\n","      <td>1.000000</td>\n","      <td>27400.0</td>\n","      <td>325.0</td>\n","      <td>650.0</td>\n","      <td>537.254902</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1275.000000</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Vishweshwara Nagar,Mysore</td>\n","      <td>12.274538</td>\n","      <td>76.644605</td>\n","      <td>51.0</td>\n","      <td>25</td>\n","      <td>4186</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>637.0</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.722222</td>\n","      <td>18</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0.055556</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>2.666667</td>\n","      <td>0.0</td>\n","      <td>2.649047e+04</td>\n","      <td>885.156605</td>\n","      <td>2.438529e+03</td>\n","      <td>1.471693e+03</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>183</td>\n","      <td>94</td>\n","      <td>2</td>\n","      <td>0.513661</td>\n","      <td>183</td>\n","      <td>11</td>\n","      <td>0.060109</td>\n","      <td>23</td>\n","      <td>0.125683</td>\n","      <td>2.504359e+05</td>\n","      <td>60.000375</td>\n","      <td>1368.502102</td>\n","      <td>175</td>\n","      <td>0.956284</td>\n","      <td>103377.0</td>\n","      <td>12.0</td>\n","      <td>995.0</td>\n","      <td>564.901639</td>\n","      <td>11</td>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.727273</td>\n","      <td>11</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.363636</td>\n","      <td>28</td>\n","      <td>3</td>\n","      <td>2.545455</td>\n","      <td>14025.000000</td>\n","      <td>1275.000000</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>0.909091</td>\n","      <td>5735.0</td>\n","      <td>425.0</td>\n","      <td>637.0</td>\n","      <td>521.363636</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>933.159722</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Jigani,Bangalore</td>\n","      <td>12.778033</td>\n","      <td>77.632191</td>\n","      <td>43.0</td>\n","      <td>16</td>\n","      <td>6212</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>466.0</td>\n","      <td>82</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>41</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.073171</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>2.292683</td>\n","      <td>0.0</td>\n","      <td>4.289350e+04</td>\n","      <td>555.041628</td>\n","      <td>1.536127e+03</td>\n","      <td>1.046183e+03</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>14260</td>\n","      <td>8063</td>\n","      <td>2</td>\n","      <td>0.565428</td>\n","      <td>14260</td>\n","      <td>1725</td>\n","      <td>0.120968</td>\n","      <td>3794</td>\n","      <td>0.266059</td>\n","      <td>8.633606e+07</td>\n","      <td>30.000035</td>\n","      <td>6054.422061</td>\n","      <td>13710</td>\n","      <td>0.961431</td>\n","      <td>55278406.0</td>\n","      <td>10.0</td>\n","      <td>40163934.0</td>\n","      <td>3876.466059</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2.000000</td>\n","      <td>933.159722</td>\n","      <td>933.159722</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1.000000</td>\n","      <td>466.0</td>\n","      <td>466.0</td>\n","      <td>466.0</td>\n","      <td>466.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 90 columns</p>\n","</div>"],"text/plain":["   POSTED_BY  UNDER_CONSTRUCTION  ...  SQUARE_FT/BHK_NO._max  SQUARE_FT/BHK_NO._mean\n","0          0                   0  ...                  650.0              537.254902\n","1          1                   0  ...                  637.0              521.363636\n","2          0                   0  ...                  466.0              466.000000\n","\n","[3 rows x 90 columns]"]},"metadata":{"tags":[]},"execution_count":181}]},{"cell_type":"code","metadata":{"id":"sdYE-WU-oC0Y","executionInfo":{"status":"ok","timestamp":1603537930764,"user_tz":-330,"elapsed":1105,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["new_df = new_df.drop(['ADDRESS'], axis=1)"],"execution_count":182,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZDjiCVfMrrnX","executionInfo":{"status":"ok","timestamp":1603537931950,"user_tz":-330,"elapsed":1367,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["featurs = [c for c in new_df.columns if c not in ['TARGET(PRICE_IN_LACS)']]"],"execution_count":183,"outputs":[]},{"cell_type":"code","metadata":{"id":"gyCSulurI_EE","executionInfo":{"status":"ok","timestamp":1603537934635,"user_tz":-330,"elapsed":1240,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"6684a259-43a1-4132-c938-710d619c381f","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["featurs"],"execution_count":184,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['POSTED_BY',\n"," 'UNDER_CONSTRUCTION',\n"," 'RERA',\n"," 'BHK_NO.',\n"," 'BHK_OR_RK',\n"," 'SQUARE_FT',\n"," 'READY_TO_MOVE',\n"," 'RESALE',\n"," 'LONGITUDE',\n"," 'LATITUDE',\n"," 'address_count',\n"," 'address_ct',\n"," 'add_group_RESALE',\n"," 'adr_1',\n"," 'adr_2',\n"," 'SQUARE_FT/BHK_NO.',\n"," 'adr_1_sum_x',\n"," 'adr_1_min_x',\n"," 'adr_1_max_x',\n"," 'POSTED_BY_count_x',\n"," 'POSTED_BY_sum_x',\n"," 'POSTED_BY_min_x',\n"," 'POSTED_BY_max_x',\n"," 'POSTED_BY_mean_x',\n"," 'UNDER_CONSTRUCTION_count_x',\n"," 'UNDER_CONSTRUCTION_sum_x',\n"," 'UNDER_CONSTRUCTION_mean_x',\n"," 'RERA_sum_x',\n"," 'RERA_min_x',\n"," 'RERA_mean_x',\n"," 'BHK_NO._min',\n"," 'BHK_NO._max_x',\n"," 'BHK_NO._mean_x',\n"," 'BHK_OR_RK_mean',\n"," 'SQUARE_FT_sum_x',\n"," 'SQUARE_FT_min_x',\n"," 'SQUARE_FT_max',\n"," 'SQUARE_FT_mean_x',\n"," 'RESALE_sum_x',\n"," 'RESALE_min_x',\n"," 'SQUARE_FT/BHK_NO._sum_x',\n"," 'SQUARE_FT/BHK_NO._min_x',\n"," 'SQUARE_FT/BHK_NO._max_x',\n"," 'SQUARE_FT/BHK_NO._mean_x',\n"," 'adr_1_sum_y',\n"," 'adr_1_min_y',\n"," 'adr_1_max_y',\n"," 'adr_2_sum',\n"," 'adr_2_min',\n"," 'POSTED_BY_count_y',\n"," 'POSTED_BY_sum_y',\n"," 'POSTED_BY_max_y',\n"," 'POSTED_BY_mean_y',\n"," 'UNDER_CONSTRUCTION_count_y',\n"," 'UNDER_CONSTRUCTION_sum_y',\n"," 'UNDER_CONSTRUCTION_mean_y',\n"," 'RERA_sum_y',\n"," 'RERA_mean_y',\n"," 'SQUARE_FT_sum_y',\n"," 'SQUARE_FT_min_y',\n"," 'SQUARE_FT_mean_y',\n"," 'RESALE_sum_y',\n"," 'RESALE_mean_x',\n"," 'SQUARE_FT/BHK_NO._sum_y',\n"," 'SQUARE_FT/BHK_NO._min_y',\n"," 'SQUARE_FT/BHK_NO._max_y',\n"," 'SQUARE_FT/BHK_NO._mean_y',\n"," 'POSTED_BY_count',\n"," 'POSTED_BY_sum',\n"," 'POSTED_BY_min_y',\n"," 'POSTED_BY_max',\n"," 'POSTED_BY_mean',\n"," 'RERA_count',\n"," 'RERA_sum',\n"," 'RERA_min_y',\n"," 'RERA_mean',\n"," 'BHK_NO._sum',\n"," 'BHK_NO._max_y',\n"," 'BHK_NO._mean_y',\n"," 'SQUARE_FT_sum',\n"," 'SQUARE_FT_min',\n"," 'RESALE_sum',\n"," 'RESALE_min_y',\n"," 'RESALE_mean_y',\n"," 'SQUARE_FT/BHK_NO._sum',\n"," 'SQUARE_FT/BHK_NO._min',\n"," 'SQUARE_FT/BHK_NO._max',\n"," 'SQUARE_FT/BHK_NO._mean']"]},"metadata":{"tags":[]},"execution_count":184}]},{"cell_type":"code","metadata":{"id":"B2UYpEzAuOYL","executionInfo":{"status":"ok","timestamp":1603537938739,"user_tz":-330,"elapsed":1547,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"4819e429-fd33-41a5-e73a-d5b5528b57e2","colab":{"base_uri":"https://localhost:8080/","height":253}},"source":["new_df[featurs+[TARGET_COL]] = new_df[featurs+[TARGET_COL]].apply(lambda x: np.log1p(x))\n","new_df[featurs+[TARGET_COL]].head()"],"execution_count":185,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>POSTED_BY</th>\n","      <th>UNDER_CONSTRUCTION</th>\n","      <th>RERA</th>\n","      <th>BHK_NO.</th>\n","      <th>BHK_OR_RK</th>\n","      <th>SQUARE_FT</th>\n","      <th>READY_TO_MOVE</th>\n","      <th>RESALE</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>address_count</th>\n","      <th>address_ct</th>\n","      <th>add_group_RESALE</th>\n","      <th>adr_1</th>\n","      <th>adr_2</th>\n","      <th>SQUARE_FT/BHK_NO.</th>\n","      <th>adr_1_sum_x</th>\n","      <th>adr_1_min_x</th>\n","      <th>adr_1_max_x</th>\n","      <th>POSTED_BY_count_x</th>\n","      <th>POSTED_BY_sum_x</th>\n","      <th>POSTED_BY_min_x</th>\n","      <th>POSTED_BY_max_x</th>\n","      <th>POSTED_BY_mean_x</th>\n","      <th>UNDER_CONSTRUCTION_count_x</th>\n","      <th>UNDER_CONSTRUCTION_sum_x</th>\n","      <th>UNDER_CONSTRUCTION_mean_x</th>\n","      <th>RERA_sum_x</th>\n","      <th>RERA_min_x</th>\n","      <th>RERA_mean_x</th>\n","      <th>BHK_NO._min</th>\n","      <th>BHK_NO._max_x</th>\n","      <th>BHK_NO._mean_x</th>\n","      <th>BHK_OR_RK_mean</th>\n","      <th>SQUARE_FT_sum_x</th>\n","      <th>SQUARE_FT_min_x</th>\n","      <th>SQUARE_FT_max</th>\n","      <th>SQUARE_FT_mean_x</th>\n","      <th>RESALE_sum_x</th>\n","      <th>RESALE_min_x</th>\n","      <th>...</th>\n","      <th>POSTED_BY_count_y</th>\n","      <th>POSTED_BY_sum_y</th>\n","      <th>POSTED_BY_max_y</th>\n","      <th>POSTED_BY_mean_y</th>\n","      <th>UNDER_CONSTRUCTION_count_y</th>\n","      <th>UNDER_CONSTRUCTION_sum_y</th>\n","      <th>UNDER_CONSTRUCTION_mean_y</th>\n","      <th>RERA_sum_y</th>\n","      <th>RERA_mean_y</th>\n","      <th>SQUARE_FT_sum_y</th>\n","      <th>SQUARE_FT_min_y</th>\n","      <th>SQUARE_FT_mean_y</th>\n","      <th>RESALE_sum_y</th>\n","      <th>RESALE_mean_x</th>\n","      <th>SQUARE_FT/BHK_NO._sum_y</th>\n","      <th>SQUARE_FT/BHK_NO._min_y</th>\n","      <th>SQUARE_FT/BHK_NO._max_y</th>\n","      <th>SQUARE_FT/BHK_NO._mean_y</th>\n","      <th>POSTED_BY_count</th>\n","      <th>POSTED_BY_sum</th>\n","      <th>POSTED_BY_min_y</th>\n","      <th>POSTED_BY_max</th>\n","      <th>POSTED_BY_mean</th>\n","      <th>RERA_count</th>\n","      <th>RERA_sum</th>\n","      <th>RERA_min_y</th>\n","      <th>RERA_mean</th>\n","      <th>BHK_NO._sum</th>\n","      <th>BHK_NO._max_y</th>\n","      <th>BHK_NO._mean_y</th>\n","      <th>SQUARE_FT_sum</th>\n","      <th>SQUARE_FT_min</th>\n","      <th>RESALE_sum</th>\n","      <th>RESALE_min_y</th>\n","      <th>RESALE_mean_y</th>\n","      <th>SQUARE_FT/BHK_NO._sum</th>\n","      <th>SQUARE_FT/BHK_NO._min</th>\n","      <th>SQUARE_FT/BHK_NO._max</th>\n","      <th>SQUARE_FT/BHK_NO._mean</th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","  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<td>16.380460</td>\n","      <td>15.687413</td>\n","      <td>1.098612</td>\n","      <td>0.693147</td>\n","      <td>...</td>\n","      <td>9.565284</td>\n","      <td>8.995165</td>\n","      <td>1.098612</td>\n","      <td>0.448159</td>\n","      <td>9.565284</td>\n","      <td>7.453562</td>\n","      <td>0.114192</td>\n","      <td>8.241440</td>\n","      <td>0.235909</td>\n","      <td>18.273758</td>\n","      <td>3.433988</td>\n","      <td>8.708709</td>\n","      <td>9.525954</td>\n","      <td>0.673674</td>\n","      <td>17.827893</td>\n","      <td>2.397895</td>\n","      <td>17.508480</td>\n","      <td>8.262937</td>\n","      <td>3.951244</td>\n","      <td>2.484907</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>0.195309</td>\n","      <td>3.951244</td>\n","      <td>1.386294</td>\n","      <td>0.000000</td>\n","      <td>0.057158</td>\n","      <td>4.867534</td>\n","      <td>1.609438</td>\n","      <td>1.261131</td>\n","      <td>11.102142</td>\n","      <td>7.171070</td>\n","      <td>3.951244</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>10.218335</td>\n","      <td>5.786897</td>\n","      <td>6.478510</td>\n","      <td>6.288332</td>\n","      <td>4.025352</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.098612</td>\n","      <td>0.0</td>\n","      <td>7.151485</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>2.585848</td>\n","      <td>4.352142</td>\n","      <td>3.258097</td>\n","      <td>8.339740</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>6.458338</td>\n","      <td>2.944439</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>2.944439</td>\n","      <td>2.639057</td>\n","      <td>0.0</td>\n","      <td>0.693147</td>\n","      <td>0.543615</td>\n","      <td>2.944439</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>0.0</td>\n","      <td>0.054067</td>\n","      <td>1.098612</td>\n","      <td>1.791759</td>\n","      <td>1.299283</td>\n","      <td>0.0</td>\n","      <td>10.184578</td>\n","      <td>6.786894</td>\n","      <td>7.799560</td>\n","      <td>7.294848</td>\n","      <td>2.944439</td>\n","      <td>0.693147</td>\n","      <td>...</td>\n","      <td>5.214936</td>\n","      <td>4.553877</td>\n","      <td>1.098612</td>\n","      <td>0.414531</td>\n","      <td>5.214936</td>\n","      <td>2.484907</td>\n","      <td>0.058372</td>\n","      <td>3.178054</td>\n","      <td>0.118390</td>\n","      <td>12.430962</td>\n","      <td>4.110880</td>\n","      <td>7.222203</td>\n","      <td>5.170484</td>\n","      <td>0.671047</td>\n","      <td>11.546147</td>\n","      <td>2.564949</td>\n","      <td>6.903747</td>\n","      <td>6.338420</td>\n","      <td>2.484907</td>\n","      <td>2.197225</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>0.546544</td>\n","      <td>2.484907</td>\n","      <td>1.609438</td>\n","      <td>0.000000</td>\n","      <td>0.310155</td>\n","      <td>3.367296</td>\n","      <td>1.386294</td>\n","      <td>1.265666</td>\n","      <td>9.548668</td>\n","      <td>7.151485</td>\n","      <td>2.397895</td>\n","      <td>0.000000</td>\n","      <td>0.646627</td>\n","      <td>8.654517</td>\n","      <td>6.054439</td>\n","      <td>6.458338</td>\n","      <td>6.258364</td>\n","      <td>3.951244</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.098612</td>\n","      <td>0.0</td>\n","      <td>6.839647</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>2.623076</td>\n","      <td>4.364781</td>\n","      <td>2.833213</td>\n","      <td>8.734399</td>\n","      <td>0.693147</td>\n","      <td>1.098612</td>\n","      <td>0.000000</td>\n","      <td>6.146329</td>\n","      <td>4.418841</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>3.737670</td>\n","      <td>1.386294</td>\n","      <td>0.0</td>\n","      <td>0.693147</td>\n","      <td>0.070618</td>\n","      <td>3.737670</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>1.386294</td>\n","      <td>1.191703</td>\n","      <td>0.0</td>\n","      <td>10.666499</td>\n","      <td>6.320843</td>\n","      <td>7.337671</td>\n","      <td>6.953859</td>\n","      <td>3.737670</td>\n","      <td>0.693147</td>\n","      <td>...</td>\n","      <td>9.565284</td>\n","      <td>8.995165</td>\n","      <td>1.098612</td>\n","      <td>0.448159</td>\n","      <td>9.565284</td>\n","      <td>7.453562</td>\n","      <td>0.114192</td>\n","      <td>8.241440</td>\n","      <td>0.235909</td>\n","      <td>18.273758</td>\n","      <td>3.433988</td>\n","      <td>8.708709</td>\n","      <td>9.525954</td>\n","      <td>0.673674</td>\n","      <td>17.827893</td>\n","      <td>2.397895</td>\n","      <td>17.508480</td>\n","      <td>8.262937</td>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>6.839647</td>\n","      <td>6.839647</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>6.146329</td>\n","      <td>6.146329</td>\n","      <td>6.146329</td>\n","      <td>6.146329</td>\n","      <td>3.784190</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>1.098612</td>\n","      <td>0.0</td>\n","      <td>6.836175</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>3.389202</td>\n","      <td>4.361116</td>\n","      <td>3.332205</td>\n","      <td>7.984463</td>\n","      <td>0.693147</td>\n","      <td>1.386294</td>\n","      <td>1.098612</td>\n","      <td>6.142037</td>\n","      <td>4.890349</td>\n","      <td>1.386294</td>\n","      <td>1.386294</td>\n","      <td>3.806662</td>\n","      <td>3.295837</td>\n","      <td>0.0</td>\n","      <td>0.693147</td>\n","      <td>0.464306</td>\n","      <td>3.806662</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.526361</td>\n","      <td>0.0</td>\n","      <td>0.559616</td>\n","      <td>0.693147</td>\n","      <td>1.386294</td>\n","      <td>1.157453</td>\n","      <td>0.0</td>\n","      <td>10.763770</td>\n","      <td>5.707210</td>\n","      <td>7.421182</td>\n","      <td>6.980490</td>\n","      <td>3.806662</td>\n","      <td>0.693147</td>\n","      <td>...</td>\n","      <td>8.189245</td>\n","      <td>7.475906</td>\n","      <td>1.098612</td>\n","      <td>0.398685</td>\n","      <td>8.189245</td>\n","      <td>5.198497</td>\n","      <td>0.048777</td>\n","      <td>7.212294</td>\n","      <td>0.319387</td>\n","      <td>15.342862</td>\n","      <td>3.496508</td>\n","      <td>7.154677</td>\n","      <td>8.173293</td>\n","      <td>0.685201</td>\n","      <td>14.455983</td>\n","      <td>3.496508</td>\n","      <td>8.760610</td>\n","      <td>6.268912</td>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>6.836175</td>\n","      <td>6.836175</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>6.142037</td>\n","      <td>6.142037</td>\n","      <td>6.142037</td>\n","      <td>6.142037</td>\n","      <td>4.151040</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>1.098612</td>\n","      <td>0.0</td>\n","      <td>6.907765</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>3.160916</td>\n","      <td>4.494070</td>\n","      <td>2.833213</td>\n","      <td>8.734399</td>\n","      <td>1.098612</td>\n","      <td>1.609438</td>\n","      <td>1.386294</td>\n","      <td>6.214608</td>\n","      <td>7.526718</td>\n","      <td>1.609438</td>\n","      <td>1.609438</td>\n","      <td>6.142037</td>\n","      <td>6.068426</td>\n","      <td>0.0</td>\n","      <td>1.098612</td>\n","      <td>0.656939</td>\n","      <td>6.142037</td>\n","      <td>4.418841</td>\n","      <td>0.162734</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.693147</td>\n","      <td>1.609438</td>\n","      <td>1.322330</td>\n","      <td>0.0</td>\n","      <td>13.975629</td>\n","      <td>5.993961</td>\n","      <td>12.119422</td>\n","      <td>7.836139</td>\n","      <td>6.079933</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>8.669399</td>\n","      <td>8.291045</td>\n","      <td>1.098612</td>\n","      <td>0.521726</td>\n","      <td>8.669399</td>\n","      <td>6.804615</td>\n","      <td>0.143914</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>15.986698</td>\n","      <td>4.700489</td>\n","      <td>7.318134</td>\n","      <td>8.631949</td>\n","      <td>0.674594</td>\n","      <td>14.998702</td>\n","      <td>4.007333</td>\n","      <td>11.035390</td>\n","      <td>6.331256</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>1.098612</td>\n","      <td>6.907765</td>\n","      <td>6.907765</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>0.693147</td>\n","      <td>6.214608</td>\n","      <td>6.214608</td>\n","      <td>6.214608</td>\n","      <td>6.214608</td>\n","      <td>4.119037</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 89 columns</p>\n","</div>"],"text/plain":["   POSTED_BY  UNDER_CONSTRUCTION  ...  SQUARE_FT/BHK_NO._mean  TARGET(PRICE_IN_LACS)\n","0   0.000000            0.000000  ...                6.288332               4.025352\n","1   0.693147            0.000000  ...                6.258364               3.951244\n","2   0.000000            0.000000  ...                6.146329               3.784190\n","3   0.000000            0.000000  ...                6.142037               4.151040\n","4   0.693147            0.693147  ...                6.214608               4.119037\n","\n","[5 rows x 89 columns]"]},"metadata":{"tags":[]},"execution_count":185}]},{"cell_type":"code","metadata":{"id":"dhnXOtC-zTZF","executionInfo":{"status":"ok","timestamp":1603537943098,"user_tz":-330,"elapsed":1237,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"2338aa0e-cd9e-4729-81da-06725b9338a4","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["new_df.isnull().sum().sum()"],"execution_count":186,"outputs":[{"output_type":"execute_result","data":{"text/plain":["69028"]},"metadata":{"tags":[]},"execution_count":186}]},{"cell_type":"code","metadata":{"id":"qp-OcaLvECRI","executionInfo":{"status":"ok","timestamp":1603537944405,"user_tz":-330,"elapsed":1406,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["new_df['LATITUDE'] = new_df['LATITUDE'].fillna(new_df['LATITUDE'].median())\n","new_df['LONGITUDE'] = new_df['LONGITUDE'].fillna(new_df['LONGITUDE'].median())"],"execution_count":187,"outputs":[]},{"cell_type":"code","metadata":{"id":"4yT-F2e5HW-L","executionInfo":{"status":"ok","timestamp":1603537945732,"user_tz":-330,"elapsed":1514,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"40c731eb-c52c-4d25-da00-fbb2bf605ef1","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["new_df.isnull().sum().sum()"],"execution_count":188,"outputs":[{"output_type":"execute_result","data":{"text/plain":["68720"]},"metadata":{"tags":[]},"execution_count":188}]},{"cell_type":"code","metadata":{"id":"09WtTt6dgDgv","executionInfo":{"status":"ok","timestamp":1603537273091,"user_tz":-330,"elapsed":1716,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["from sklearn.metrics import mean_squared_error, mean_squared_log_error\n","\n","def rmsle(y_test, y_pred):\n","  return np.sqrt(mean_squared_log_error(y_test, y_pred))\n","\n","def av_metrices(y_test, y_pred):\n","  return np.sqrt(mean_squared_error(y_test, y_pred))"],"execution_count":120,"outputs":[]},{"cell_type":"code","metadata":{"id":"f_5T0rzRgFEl","executionInfo":{"status":"ok","timestamp":1603537949064,"user_tz":-330,"elapsed":1589,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["trn_proc, test_proc = new_df[:train.shape[0]], new_df[train.shape[0]:].reset_index(drop = True)\n","features = [c for c in trn_proc.columns if c not in [TARGET_COL]]"],"execution_count":189,"outputs":[]},{"cell_type":"code","metadata":{"id":"82pS-yBwtftf","executionInfo":{"status":"ok","timestamp":1603537949911,"user_tz":-330,"elapsed":947,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["test_proc = test_proc.drop(['TARGET(PRICE_IN_LACS)'], axis=1)"],"execution_count":190,"outputs":[]},{"cell_type":"code","metadata":{"id":"LqIaHj7Wg3wg","executionInfo":{"status":"ok","timestamp":1603537953271,"user_tz":-330,"elapsed":1375,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["trn, val = train_test_split(trn_proc, test_size=0.2, random_state=1)\n","\n","X_trn, X_val = trn[features], val[features]\n","\n","y_trn, y_val = trn[TARGET_COL], val[TARGET_COL]\n","\n","X_test = test_proc[features]"],"execution_count":191,"outputs":[]},{"cell_type":"code","metadata":{"id":"CWSNHJW0nTIk","executionInfo":{"status":"ok","timestamp":1603532487564,"user_tz":-330,"elapsed":1364,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["from sklearn.model_selection import StratifiedKFold\n","\n","def clf_run(clf, train, test, features):\n","  N_SPLITS = 5\n","\n","  oofs = np.zeros(len(train))\n","  preds = np.zeros(len(test))\n","\n","  target = train[TARGET_COL]\n","\n","  folds = StratifiedKFold(n_splits=N_SPLITS)\n","  stratified_target = pd.qcut(train[TARGET_COL], 10, labels=False, duplicates='drop')\n","\n","  for fold_, (trn_idx, val_idx) in enumerate(folds.split(train, stratified_target)):\n","    print(f'\\n------------- Fold {fold_ + 1} -------------')\n","\n","    ############# Get train, validation and test sets along with targets ################\n","  \n","    ### Training Set\n","    X_trn, y_trn = train[features].iloc[trn_idx], target.iloc[trn_idx]\n","\n","    ### Validation Set\n","    X_val, y_val = train[features].iloc[val_idx], target.iloc[val_idx]\n","\n","    ### Test Set\n","    X_test = test[features]\n","\n","    ############# Scaling Data ################\n","    scaler = StandardScaler()\n","    _ = scaler.fit(X_trn)\n","\n","    X_trn = scaler.transform(X_trn)\n","    X_val = scaler.transform(X_val)\n","    X_test = scaler.transform(X_test)\n","\n","    ############# Fitting and Predicting ################\n","\n","    _ = clf.fit(X_trn, y_trn)\n","\n","    ### Instead of directly predicting the classes we will obtain the probability of positive class.\n","    preds_val = clf.predict(X_val)\n","    preds_test = clf.predict(X_test)\n","\n","    fold_score = av_metrices(y_val, preds_val)\n","    print(f'\\nAV metric score for validation set is {fold_score}')\n","\n","    oofs[val_idx] = preds_val\n","    preds += preds_test / N_SPLITS\n","\n","\n","  oofs_score = av_metrices(target, oofs)\n","  print(f'\\n\\nAV metric for oofs is {oofs_score}')\n","\n","  return oofs, preds"],"execution_count":37,"outputs":[]},{"cell_type":"code","metadata":{"id":"HT6PNDuvoMWF","executionInfo":{"status":"ok","timestamp":1603532490926,"user_tz":-330,"elapsed":1175,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["def run_gradient_boosting(clf, fit_params, train, test, features):\n","  N_SPLITS = 5\n","  oofs = np.zeros(len(trn_proc))\n","  preds = np.zeros((len(test_proc)))\n","\n","  target = train[TARGET_COL]\n","\n","  folds = StratifiedKFold(n_splits = N_SPLITS)\n","  stratified_target = pd.qcut(train[TARGET_COL], 10, labels = False, duplicates='drop')\n","\n","  feature_importances = pd.DataFrame()\n","\n","  for fold_, (trn_idx, val_idx) in enumerate(folds.split(train, stratified_target)):\n","    print(f'\\n------------- Fold {fold_ + 1} -------------')\n","\n","    ### Training Set\n","    X_trn, y_trn = train[features].iloc[trn_idx], target.iloc[trn_idx]\n","\n","    ### Validation Set\n","    X_val, y_val = train[features].iloc[val_idx], target.iloc[val_idx]\n","\n","    ### Test Set\n","    X_test = test[features]\n","\n","    scaler = StandardScaler()\n","    _ = scaler.fit(X_trn)\n","\n","    X_trn = scaler.transform(X_trn)\n","    X_val = scaler.transform(X_val)\n","    X_test = scaler.transform(X_test)\n","    \n","    _ = clf.fit(X_trn, y_trn, eval_set = [(X_val, y_val)], **fit_params)\n","\n","    fold_importance = pd.DataFrame({'fold': fold_ + 1, 'feature': features, 'importance': clf.feature_importances_})\n","    feature_importances = pd.concat([feature_importances, fold_importance], axis=0)\n","\n","    ### Instead of directly predicting the classes we will obtain the probability of positive class.\n","    preds_val = clf.predict(X_val)\n","    preds_test = clf.predict(X_test)\n","\n","    fold_score = av_metrices(y_val, preds_val)\n","    print(f'\\nAV metric score for validation set is {fold_score}')\n","\n","    oofs[val_idx] = preds_val\n","    preds += preds_test / N_SPLITS\n","\n","\n","  oofs_score = av_metrices(target, oofs)\n","  print(f'\\n\\nAV metric for oofs is {oofs_score}')\n","\n","  feature_importances = feature_importances.reset_index(drop = True)\n","  fi = feature_importances.groupby('feature')['importance'].mean().sort_values(ascending = False)[:30][::-1]\n","  fi.plot(kind = 'barh', figsize=(12, 6))\n","\n","  return oofs, preds, fi"],"execution_count":38,"outputs":[]},{"cell_type":"code","metadata":{"id":"SraTwfHBJW3M"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_nmmaeNzSTB4","executionInfo":{"status":"ok","timestamp":1603537962966,"user_tz":-330,"elapsed":1212,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["plt.style.use('seaborn-whitegrid')"],"execution_count":192,"outputs":[]},{"cell_type":"code","metadata":{"id":"s2yRkRoVpkw4","executionInfo":{"status":"ok","timestamp":1603532544791,"user_tz":-330,"elapsed":1179,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["hyper_space = {'n_estimators': [1000, 1500, 2000],\n","               'max_depth':  [4, 5, 7, -1],\n","               'num_leaves': [15, 31, 63, 127],\n","               'subsample': [0.6, 0.7, 0.8, 1.0],\n","               'colsample_bytree': [0.6, 0.7, 0.8, 1.0]}"],"execution_count":40,"outputs":[]},{"cell_type":"code","metadata":{"id":"-i7OWUTtq3Hm"},"source":["clf = RandomizedSearchCV(LGBMRegressor(),\n","                         hyper_space,\n","                         scoring='neg_root_mean_squared_error',\n","                         random_state=1,\n","                         n_iter=20)\n","\n","lgbm = clf.fit(trn_proc[features], trn_proc[TARGET_COL])\n","lgbm.best_params_"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qbjvJ4D3AmuG"},"source":["\n","          "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DWHXyDQ8SSyg","executionInfo":{"status":"ok","timestamp":1603538019644,"user_tz":-330,"elapsed":42366,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"ccce3034-b4f5-41f8-ff28-752c4bda4131","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["clf = LGBMRegressor(learning_rate = 0.32,\n","               max_depth = 6,\n","               min_data_in_leaf = 20,\n","               num_leaves = 300,\n","               subsample = 0.6,\n","               colsample_bytree = 0.7,\n","               boosting_type= 'gbdt',\n","               objective= 'regression',\n","               reg_alpha = 0.67,\n","               reg_lambda = 0.1,\n","               n_estimators = 2000,\n","               metric = None,\n","               feature_fraction= 0.9,\n","               bagging_fraction= .9,\n","               bagging_freq= 70)\n","fit_params = {'verbose': 200, 'early_stopping_rounds': 200, 'eval_metric': 'rmse'}\n","\n","lgb_oofs, lgb_preds, fi = run_gradient_boosting(clf, fit_params, trn_proc, test_proc, features)"],"execution_count":193,"outputs":[{"output_type":"stream","text":["\n","------------- Fold 1 -------------\n","Training until validation scores don't improve for 200 rounds.\n","[200]\tvalid_0's rmse: 0.281101\n","Early stopping, best iteration is:\n","[162]\tvalid_0's rmse: 0.280623\n","\n","AV metric score for validation set is 0.2806231711918016\n","\n","------------- Fold 2 -------------\n","Training until validation scores don't improve for 200 rounds.\n","[200]\tvalid_0's rmse: 0.283554\n","[400]\tvalid_0's rmse: 0.283235\n","Early stopping, best iteration is:\n","[244]\tvalid_0's rmse: 0.283086\n","\n","AV metric score for validation set is 0.2830857530337283\n","\n","------------- Fold 3 -------------\n","Training until validation scores don't improve for 200 rounds.\n","[200]\tvalid_0's rmse: 0.279731\n","[400]\tvalid_0's rmse: 0.2805\n","Early stopping, best iteration is:\n","[255]\tvalid_0's rmse: 0.278726\n","\n","AV metric score for validation set is 0.2787262473552032\n","\n","------------- Fold 4 -------------\n","Training until validation scores don't improve for 200 rounds.\n","[200]\tvalid_0's rmse: 0.281994\n","Early stopping, best iteration is:\n","[197]\tvalid_0's rmse: 0.281706\n","\n","AV metric score for validation set is 0.2817064175242017\n","\n","------------- Fold 5 -------------\n","Training until validation scores don't improve for 200 rounds.\n","[200]\tvalid_0's rmse: 0.303552\n","[400]\tvalid_0's rmse: 0.302852\n","Early stopping, best iteration is:\n","[307]\tvalid_0's rmse: 0.301998\n","\n","AV metric score for validation set is 0.3019983457638543\n","\n","\n","AV metric for oofs is 0.2853543154457535\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rxVH9IXTBx7z","executionInfo":{"status":"ok","timestamp":1603538059452,"user_tz":-330,"elapsed":1646,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["feat = pd.DataFrame()\n","feat['col'] = features\n","feat['import'] = clf.feature_importances_"],"execution_count":194,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDwa5oyuDR2n","executionInfo":{"status":"ok","timestamp":1603538059454,"user_tz":-330,"elapsed":772,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"dfb9c27a-0ac9-4b51-89e4-c31a89c2f7ee","colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["clf.feature_importances_"],"execution_count":195,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 44,  37,  21,  52,   2, 582,   3,   4, 422, 439, 269, 259,   8,\n","       412, 109, 493, 439,  53,   1, 118, 138,   8,  20, 311,   8, 121,\n","       220, 111,   4, 230,   9,  57, 364,  20, 224, 390, 285, 338, 109,\n","         5, 177, 401, 339, 299,  86,  62, 116, 175,  13,  49,  73,   1,\n","       124,   4,  46, 134,  78, 116,  43, 121, 112,  13, 123,  30,  92,\n","        93, 101,  64, 100,  11,  11, 284,  12,  88,   2, 265, 138,  49,\n","       227, 339,  53,  54,   4, 111, 322, 443, 375, 410])"]},"metadata":{"tags":[]},"execution_count":195}]},{"cell_type":"code","metadata":{"id":"eb1Zm5KkDHPp","executionInfo":{"status":"ok","timestamp":1603538074323,"user_tz":-330,"elapsed":1216,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"6b521e86-ca7c-4270-bc1b-2fdc6bff9dd5","colab":{"base_uri":"https://localhost:8080/","height":111}},"source":["f_name = feat.sort_values(by='import')\n","f_name[f_name['import']==1]"],"execution_count":197,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>col</th>\n","      <th>import</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>18</th>\n","      <td>adr_1_max_x</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>POSTED_BY_max_y</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                col  import\n","18      adr_1_max_x       1\n","51  POSTED_BY_max_y       1"]},"metadata":{"tags":[]},"execution_count":197}]},{"cell_type":"code","metadata":{"id":"ySWnRa36S0xV","executionInfo":{"status":"ok","timestamp":1603538080798,"user_tz":-330,"elapsed":1710,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["lgb_preds_t = np.expm1(lgb_preds)\n","sol_fl = pd.DataFrame()\n","sol_fl[TARGET_COL] = lgb_preds_t\n","sol_fl.to_csv(\"sol.csv\", index=False)"],"execution_count":198,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"m2RXiUNuTL9E"},"source":["# CatBoost"]},{"cell_type":"code","metadata":{"id":"3xsq00aITnJY","executionInfo":{"status":"ok","timestamp":1603538089201,"user_tz":-330,"elapsed":1567,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["cat_params = {'iterations': np.linspace(10, 1000, num=20),\n","              'depth': np.linspace(1, 10, num=3),\n","              'learning_rate': np.linspace(0.01, 1.0, num=10),\n","              'random_strength': np.linspace(1e-9, 10, num=10),\n","              'bagging_temperature': np.linspace(0.0, 1.0, num=10),\n","              'border_count': np.linspace(1, 255, num=255),\n","              'l2_leaf_reg': np.linspace(2, 30, num=2)}"],"execution_count":199,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ol2CzqMaUsS3","executionInfo":{"status":"ok","timestamp":1603505396674,"user_tz":-330,"elapsed":36073,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"74a34181-3a6d-41f8-85b6-575cbc617d77","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["clf = RandomizedSearchCV(CatBoostRegressor(),\n","                         cat_params,\n","                         scoring='neg_root_mean_squared_error',\n","                         random_state=1,\n","                         n_iter=20)\n","\n","lgbm = clf.fit(trn_proc[features], trn_proc[TARGET_COL])\n","lgbm.best_params_"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","1:\tlearn: 0.6941666\ttotal: 9.99ms\tremaining: 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59.5ms\n","990:\tlearn: 0.3432270\ttotal: 5.9s\tremaining: 53.6ms\n","991:\tlearn: 0.3432177\ttotal: 5.91s\tremaining: 47.6ms\n","992:\tlearn: 0.3432001\ttotal: 5.91s\tremaining: 41.7ms\n","993:\tlearn: 0.3431902\ttotal: 5.92s\tremaining: 35.7ms\n","994:\tlearn: 0.3431775\ttotal: 5.92s\tremaining: 29.8ms\n","995:\tlearn: 0.3431566\ttotal: 5.93s\tremaining: 23.8ms\n","996:\tlearn: 0.3431505\ttotal: 5.93s\tremaining: 17.9ms\n","997:\tlearn: 0.3431395\ttotal: 5.94s\tremaining: 11.9ms\n","998:\tlearn: 0.3431198\ttotal: 5.94s\tremaining: 5.95ms\n","999:\tlearn: 0.3431073\ttotal: 5.95s\tremaining: 0us\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["{'bagging_temperature': 0.8888888888888888,\n"," 'border_count': 155.0,\n"," 'depth': 1.0,\n"," 'iterations': 1000.0,\n"," 'l2_leaf_reg': 30.0,\n"," 'learning_rate': 0.89,\n"," 'random_strength': 3.333333334}"]},"metadata":{"tags":[]},"execution_count":114}]},{"cell_type":"code","metadata":{"id":"_UCvyFTBWUvV","executionInfo":{"status":"ok","timestamp":1603538110475,"user_tz":-330,"elapsed":1345,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["cat_par = {'n_estimators': 2000,\n","           'border_count': 89.0,\n","           'depth': 6.0,\n","           'l2_leaf_reg': 30.0,\n","           'learning_rate': 0.33,\n","           'random_strength': 4.45,\n","           'rsm' : 0.4,\n","           'random_state':10}"],"execution_count":200,"outputs":[]},{"cell_type":"code","metadata":{"id":"qmSd7sIwTLiv","executionInfo":{"status":"ok","timestamp":1603538212496,"user_tz":-330,"elapsed":98767,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"8df7633e-6cce-440a-ddc1-a98d2185decb","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["clf = CatBoostRegressor(**cat_par)\n","\n","fit_params = {'verbose': 200, 'early_stopping_rounds': 200}\n","\n","cb_oofs, cb_preds, fi = run_gradient_boosting(clf, fit_params, trn_proc, test_proc, features)"],"execution_count":201,"outputs":[{"output_type":"stream","text":["\n","------------- Fold 1 -------------\n","0:\tlearn: 0.7280032\ttest: 0.7225991\tbest: 0.7225991 (0)\ttotal: 61.6ms\tremaining: 2m 3s\n","200:\tlearn: 0.2589006\ttest: 0.2839821\tbest: 0.2839821 (200)\ttotal: 2.46s\tremaining: 22.1s\n","400:\tlearn: 0.2224370\ttest: 0.2747078\tbest: 0.2746437 (399)\ttotal: 4.86s\tremaining: 19.4s\n","600:\tlearn: 0.1997748\ttest: 0.2723475\tbest: 0.2723475 (600)\ttotal: 7.27s\tremaining: 16.9s\n","800:\tlearn: 0.1816662\ttest: 0.2707303\tbest: 0.2707228 (798)\ttotal: 9.69s\tremaining: 14.5s\n","1000:\tlearn: 0.1679280\ttest: 0.2698008\tbest: 0.2696852 (994)\ttotal: 12.1s\tremaining: 12.1s\n","1200:\tlearn: 0.1563358\ttest: 0.2701545\tbest: 0.2695897 (1010)\ttotal: 14.6s\tremaining: 9.69s\n","Stopped by overfitting detector  (200 iterations wait)\n","\n","bestTest = 0.2695897211\n","bestIteration = 1010\n","\n","Shrink model to first 1011 iterations.\n","\n","AV metric score for validation set is 0.2695897204300224\n","\n","------------- Fold 2 -------------\n","0:\tlearn: 0.7248739\ttest: 0.7400598\tbest: 0.7400598 (0)\ttotal: 13.2ms\tremaining: 26.4s\n","200:\tlearn: 0.2537524\ttest: 0.2930273\tbest: 0.2930273 (200)\ttotal: 2.43s\tremaining: 21.7s\n","400:\tlearn: 0.2187748\ttest: 0.2855375\tbest: 0.2855375 (400)\ttotal: 4.79s\tremaining: 19.1s\n","600:\tlearn: 0.1972667\ttest: 0.2818080\tbest: 0.2818080 (600)\ttotal: 7.16s\tremaining: 16.7s\n","800:\tlearn: 0.1798255\ttest: 0.2809954\tbest: 0.2809132 (794)\ttotal: 9.56s\tremaining: 14.3s\n","1000:\tlearn: 0.1646036\ttest: 0.2791745\tbest: 0.2790567 (993)\ttotal: 12s\tremaining: 12s\n","1200:\tlearn: 0.1521780\ttest: 0.2782783\tbest: 0.2782694 (1192)\ttotal: 14.4s\tremaining: 9.56s\n","1400:\tlearn: 0.1415675\ttest: 0.2777445\tbest: 0.2776592 (1377)\ttotal: 16.8s\tremaining: 7.18s\n","1600:\tlearn: 0.1324391\ttest: 0.2774688\tbest: 0.2774688 (1600)\ttotal: 19.2s\tremaining: 4.79s\n","1800:\tlearn: 0.1245278\ttest: 0.2771081\tbest: 0.2769899 (1710)\ttotal: 21.7s\tremaining: 2.39s\n","Stopped by overfitting detector  (200 iterations wait)\n","\n","bestTest = 0.2769898701\n","bestIteration = 1710\n","\n","Shrink model to first 1711 iterations.\n","\n","AV metric score for validation set is 0.2769898704485898\n","\n","------------- Fold 3 -------------\n","0:\tlearn: 0.7236273\ttest: 0.7250407\tbest: 0.7250407 (0)\ttotal: 12.9ms\tremaining: 25.7s\n","200:\tlearn: 0.2553843\ttest: 0.2920501\tbest: 0.2920493 (199)\ttotal: 2.42s\tremaining: 21.6s\n","400:\tlearn: 0.2223907\ttest: 0.2806700\tbest: 0.2806669 (399)\ttotal: 4.83s\tremaining: 19.2s\n","600:\tlearn: 0.1978293\ttest: 0.2761394\tbest: 0.2761394 (600)\ttotal: 7.24s\tremaining: 16.9s\n","800:\tlearn: 0.1807586\ttest: 0.2740951\tbest: 0.2740951 (800)\ttotal: 9.65s\tremaining: 14.4s\n","1000:\tlearn: 0.1662867\ttest: 0.2730198\tbest: 0.2729542 (977)\ttotal: 12.1s\tremaining: 12s\n","1200:\tlearn: 0.1550429\ttest: 0.2726998\tbest: 0.2726885 (1089)\ttotal: 14.5s\tremaining: 9.62s\n","1400:\tlearn: 0.1438325\ttest: 0.2721995\tbest: 0.2719680 (1375)\ttotal: 16.9s\tremaining: 7.22s\n","Stopped by overfitting detector  (200 iterations wait)\n","\n","bestTest = 0.2719679596\n","bestIteration = 1375\n","\n","Shrink model to first 1376 iterations.\n","\n","AV metric score for validation set is 0.27196796065263973\n","\n","------------- Fold 4 -------------\n","0:\tlearn: 0.7258033\ttest: 0.7143406\tbest: 0.7143406 (0)\ttotal: 12.9ms\tremaining: 25.8s\n","200:\tlearn: 0.2565728\ttest: 0.2913121\tbest: 0.2913121 (200)\ttotal: 2.39s\tremaining: 21.4s\n","400:\tlearn: 0.2236616\ttest: 0.2807746\tbest: 0.2807657 (399)\ttotal: 4.76s\tremaining: 19s\n","600:\tlearn: 0.2002107\ttest: 0.2758154\tbest: 0.2757525 (592)\ttotal: 7.16s\tremaining: 16.7s\n","800:\tlearn: 0.1820630\ttest: 0.2730692\tbest: 0.2730692 (800)\ttotal: 9.54s\tremaining: 14.3s\n","1000:\tlearn: 0.1669688\ttest: 0.2717776\tbest: 0.2717444 (996)\ttotal: 12s\tremaining: 11.9s\n","1200:\tlearn: 0.1542759\ttest: 0.2711299\tbest: 0.2710970 (1166)\ttotal: 14.3s\tremaining: 9.54s\n","1400:\tlearn: 0.1431234\ttest: 0.2712573\tbest: 0.2707908 (1249)\ttotal: 16.8s\tremaining: 7.18s\n","Stopped by overfitting detector  (200 iterations wait)\n","\n","bestTest = 0.2707908042\n","bestIteration = 1249\n","\n","Shrink model to first 1250 iterations.\n","\n","AV metric score for validation set is 0.2707908054766202\n","\n","------------- Fold 5 -------------\n","0:\tlearn: 0.7220162\ttest: 0.7223136\tbest: 0.7223136 (0)\ttotal: 12.8ms\tremaining: 25.6s\n","200:\tlearn: 0.2508817\ttest: 0.3104721\tbest: 0.3104721 (200)\ttotal: 2.42s\tremaining: 21.7s\n","400:\tlearn: 0.2160953\ttest: 0.3015112\tbest: 0.3013373 (389)\ttotal: 4.82s\tremaining: 19.2s\n","600:\tlearn: 0.1934411\ttest: 0.2970744\tbest: 0.2970244 (599)\ttotal: 7.27s\tremaining: 16.9s\n","800:\tlearn: 0.1776860\ttest: 0.2966533\tbest: 0.2961425 (712)\ttotal: 9.71s\tremaining: 14.5s\n","1000:\tlearn: 0.1634617\ttest: 0.2956877\tbest: 0.2956169 (956)\ttotal: 12.2s\tremaining: 12.1s\n","1200:\tlearn: 0.1517650\ttest: 0.2951822\tbest: 0.2950022 (1165)\ttotal: 14.6s\tremaining: 9.7s\n","1400:\tlearn: 0.1414674\ttest: 0.2948313\tbest: 0.2947671 (1356)\ttotal: 17s\tremaining: 7.29s\n","1600:\tlearn: 0.1327961\ttest: 0.2950409\tbest: 0.2947667 (1401)\ttotal: 19.5s\tremaining: 4.85s\n","Stopped by overfitting detector  (200 iterations wait)\n","\n","bestTest = 0.2947666524\n","bestIteration = 1401\n","\n","Shrink model to first 1402 iterations.\n","\n","AV metric score for validation set is 0.29476665167034255\n","\n","\n","AV metric for oofs is 0.2769774233144943\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"0skerLkMXXRn"},"source":["cb_preds_t = np.expm1(cb_preds)\n","sol_fl = pd.DataFrame()\n","sol_fl[TARGET_COL] = cb_preds_t\n","sol_fl.to_csv(\"catboost.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rLb6Df5KVEpF","executionInfo":{"status":"ok","timestamp":1603538302876,"user_tz":-330,"elapsed":1204,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"f1b4785b-a92b-42ea-ae8a-5440f3ad092a","colab":{"base_uri":"https://localhost:8080/","height":391}},"source":["clf.feature_importances_"],"execution_count":202,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([3.14273833e-01, 7.15002086e-02, 6.32898040e-02, 2.17469615e+00,\n","       2.44141587e-03, 1.67919076e+01, 2.03597941e-01, 1.01474823e-02,\n","       1.83384685e+00, 3.24322434e+00, 4.35655695e-01, 4.78865708e-01,\n","       9.98125463e-03, 3.43704545e-01, 4.46380983e-01, 8.83133561e-01,\n","       5.22096010e-01, 3.13865476e-01, 2.13585334e-01, 5.70400776e-01,\n","       3.09985138e-01, 1.37444018e-01, 3.76881899e-01, 8.53204784e+00,\n","       2.25004630e-01, 6.04048543e-01, 5.79591225e-01, 2.19079047e-01,\n","       3.09312652e-02, 5.99238172e-01, 2.29893302e-02, 2.30156939e-01,\n","       2.78151933e+00, 8.77471703e-02, 2.94508294e-01, 7.53282103e-01,\n","       2.23244780e+00, 2.08795096e+00, 3.55641302e-01, 3.34957475e-02,\n","       2.97661845e-01, 8.65202640e-01, 8.75724869e-01, 1.34450264e+00,\n","       5.34090716e-01, 1.95481042e-01, 2.83340430e-01, 1.29590346e+00,\n","       6.28262245e-02, 9.61309758e-01, 3.11155062e+00, 5.83134021e-02,\n","       2.19844589e+00, 1.49063968e-01, 5.16616046e-01, 2.76181246e+00,\n","       2.74034213e-01, 4.29732038e-01, 9.40743990e+00, 2.31091309e-01,\n","       2.45183616e-01, 1.16014112e-01, 4.34369485e-01, 1.05731996e+00,\n","       3.25213478e-01, 1.98794018e-01, 2.74139547e-01, 8.44806963e-02,\n","       1.12206960e-01, 2.91164939e-01, 3.81529840e-02, 3.74620082e-01,\n","       4.41639442e-02, 1.24382348e-01, 7.59450963e-03, 2.31856473e-01,\n","       8.72649871e-02, 6.73677579e-02, 4.07706778e+00, 6.43615361e-01,\n","       8.28195291e+00, 7.14384299e-02, 1.38259876e-01, 1.94545003e+00,\n","       8.60660551e-01, 3.44628557e+00, 6.11165589e-01, 5.35084786e-01])"]},"metadata":{"tags":[]},"execution_count":202}]},{"cell_type":"markdown","metadata":{"id":"ypNz-6cDXomq"},"source":["# XGBoost"]},{"cell_type":"code","metadata":{"id":"mSHGD3lcFXf6","executionInfo":{"status":"ok","timestamp":1603538553199,"user_tz":-330,"elapsed":238577,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"5fab736a-1aa6-4cc7-ff49-433fa2ab548d","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["clf = XGBRegressor(n_estimators = 2000,\n","                    max_depth = 6,\n","                    learning_rate = 0.33,\n","                    colsample_bytree = 0.7,\n","                    random_state=10,\n","                    )\n","\n","fit_params = {'verbose': 200, 'early_stopping_rounds': 200}\n","\n","xgb_oofs, xgb_preds, fi = run_gradient_boosting(clf, fit_params, trn_proc, test_proc, features)"],"execution_count":203,"outputs":[{"output_type":"stream","text":["\n","------------- Fold 1 -------------\n","[11:18:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:2.5886\n","Will train until validation_0-rmse hasn't improved in 200 rounds.\n","[200]\tvalidation_0-rmse:0.269818\n","[400]\tvalidation_0-rmse:0.270036\n","Stopping. Best iteration:\n","[212]\tvalidation_0-rmse:0.269482\n","\n","\n","AV metric score for validation set is 0.2694825700462236\n","\n","------------- Fold 2 -------------\n","[11:19:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:2.59504\n","Will train until validation_0-rmse hasn't improved in 200 rounds.\n","[200]\tvalidation_0-rmse:0.283035\n","[400]\tvalidation_0-rmse:0.283086\n","Stopping. Best iteration:\n","[225]\tvalidation_0-rmse:0.282714\n","\n","\n","AV metric score for validation set is 0.28271364769112123\n","\n","------------- Fold 3 -------------\n","[11:19:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:2.58829\n","Will train until validation_0-rmse hasn't improved in 200 rounds.\n","[200]\tvalidation_0-rmse:0.277985\n","[400]\tvalidation_0-rmse:0.277126\n","[600]\tvalidation_0-rmse:0.276959\n","Stopping. Best iteration:\n","[447]\tvalidation_0-rmse:0.276713\n","\n","\n","AV metric score for validation set is 0.27671360112706533\n","\n","------------- Fold 4 -------------\n","[11:20:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:2.58057\n","Will train until validation_0-rmse hasn't improved in 200 rounds.\n","[200]\tvalidation_0-rmse:0.27859\n","[400]\tvalidation_0-rmse:0.276857\n","Stopping. Best iteration:\n","[259]\tvalidation_0-rmse:0.276356\n","\n","\n","AV metric score for validation set is 0.27635631618624684\n","\n","------------- Fold 5 -------------\n","[11:21:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n","[0]\tvalidation_0-rmse:2.59143\n","Will train until validation_0-rmse hasn't improved in 200 rounds.\n","[200]\tvalidation_0-rmse:0.299664\n","[400]\tvalidation_0-rmse:0.298896\n","[600]\tvalidation_0-rmse:0.299014\n","Stopping. Best iteration:\n","[439]\tvalidation_0-rmse:0.298644\n","\n","\n","AV metric score for validation set is 0.2986443273126589\n","\n","\n","AV metric for oofs is 0.2809548794146562\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"xrIhTbE9KBUl","executionInfo":{"status":"ok","timestamp":1603538564354,"user_tz":-330,"elapsed":1505,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["xg_preds = np.expm1(xgb_preds)\n","sol_fl = pd.DataFrame()\n","sol_fl[TARGET_COL] = xg_preds"],"execution_count":204,"outputs":[]},{"cell_type":"code","metadata":{"id":"gr1a8cgGKF-t","executionInfo":{"status":"ok","timestamp":1603538566729,"user_tz":-330,"elapsed":1362,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["sol_fl.to_csv(\"kFold_xg.csv\", index=False)"],"execution_count":205,"outputs":[]},{"cell_type":"code","metadata":{"id":"6S7X-B5evX6i","executionInfo":{"status":"ok","timestamp":1603538568055,"user_tz":-330,"elapsed":1416,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"acb74573-1858-441e-de7e-b680c9cec7f5","colab":{"base_uri":"https://localhost:8080/","height":561}},"source":["fi"],"execution_count":206,"outputs":[{"output_type":"execute_result","data":{"text/plain":["feature\n","RERA_min_x                    0.002699\n","RERA_sum_y                    0.002864\n","adr_1_sum_y                   0.002935\n","SQUARE_FT/BHK_NO._max_y       0.003184\n","POSTED_BY_count_y             0.003350\n","adr_1_max_y                   0.003448\n","RESALE_mean_x                 0.004616\n","RESALE_sum_y                  0.005038\n","RESALE_min_x                  0.005203\n","SQUARE_FT_mean_x              0.005570\n","SQUARE_FT/BHK_NO._min         0.005582\n","POSTED_BY_mean_x              0.006652\n","BHK_NO._mean_y                0.007921\n","POSTED_BY_max_x               0.008587\n","SQUARE_FT/BHK_NO.             0.008879\n","POSTED_BY_min_x               0.009518\n","LATITUDE                      0.009685\n","adr_2                         0.010402\n","BHK_NO.                       0.012131\n","adr_2_sum                     0.013004\n","SQUARE_FT_min                 0.017476\n","BHK_NO._mean_x                0.022457\n","RESALE                        0.037914\n","SQUARE_FT                     0.039175\n","SQUARE_FT/BHK_NO._sum_y       0.048118\n","UNDER_CONSTRUCTION_count_y    0.062223\n","UNDER_CONSTRUCTION_sum_y      0.063467\n","POSTED_BY_mean_y              0.094429\n","SQUARE_FT_sum_y               0.159118\n","POSTED_BY_sum_y               0.242777\n","Name: importance, dtype: float32"]},"metadata":{"tags":[]},"execution_count":206}]},{"cell_type":"markdown","metadata":{"id":"2UdlYusCYn_P"},"source":["# Ensembling"]},{"cell_type":"code","metadata":{"id":"foPEwQTUYsxa","executionInfo":{"status":"ok","timestamp":1603538618962,"user_tz":-330,"elapsed":1304,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"367dd3db-245c-474b-fa16-891206d8a104","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["av_metrices(np.log1p(train[TARGET_COL]), lgb_oofs*0.20 + cb_oofs*0.45 + xgb_oofs*0.35)"],"execution_count":209,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.26800482651634605"]},"metadata":{"tags":[]},"execution_count":209}]},{"cell_type":"code","metadata":{"id":"a5fpv3zJZhTf","executionInfo":{"status":"ok","timestamp":1603538647435,"user_tz":-330,"elapsed":1364,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["train_new = train[[TARGET_COL]]\n","train_new[TARGET_COL] = np.log1p(train_new[TARGET_COL])\n","\n","test_new = pd.DataFrame()\n","\n","train_new['lgb'] = 0.2*lgb_oofs\n","test_new['lgb'] = 0.2*lgb_preds\n","\n","train_new['cb'] = 0.45*cb_oofs\n","test_new['cb'] = 0.45*cb_preds\n","\n","train_new['xgb'] = 0.35*xgb_oofs\n","test_new['xgb'] = 0.35*xgb_preds\n","\n","features = [c for c in train_new.columns if c not in [TARGET_COL]]"],"execution_count":210,"outputs":[]},{"cell_type":"code","metadata":{"id":"EcDwA284ZrcK","executionInfo":{"status":"ok","timestamp":1603538652332,"user_tz":-330,"elapsed":1383,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"2f0f9da6-8c99-4007-cc17-0259a59442bf","colab":{"base_uri":"https://localhost:8080/","height":408}},"source":["from sklearn.linear_model import LinearRegression\n","clf = LinearRegression()\n","\n","ens_oofs, ens_preds = clf_run(clf, train_new, test_new, features)\n"],"execution_count":211,"outputs":[{"output_type":"stream","text":["\n","------------- Fold 1 -------------\n","\n","AV metric score for validation set is 0.2588693216275976\n","\n","------------- Fold 2 -------------\n","\n","AV metric score for validation set is 0.2688226196906159\n","\n","------------- Fold 3 -------------\n","\n","AV metric score for validation set is 0.26228374692456824\n","\n","------------- Fold 4 -------------\n","\n","AV metric score for validation set is 0.26378334321853353\n","\n","------------- Fold 5 -------------\n","\n","AV metric score for validation set is 0.2857870257208543\n","\n","\n","AV metric for oofs is 0.2680769624632477\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8eZDpwjDZ7p-","executionInfo":{"status":"ok","timestamp":1603538667587,"user_tz":-330,"elapsed":1426,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["pred = np.expm1(ens_preds)\n","sol_fl = pd.DataFrame()\n","sol_fl[TARGET_COL] = pred\n"],"execution_count":212,"outputs":[]},{"cell_type":"code","metadata":{"id":"9WpSz13WvCZy","executionInfo":{"status":"ok","timestamp":1603538681751,"user_tz":-330,"elapsed":1078,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["sol_fl.to_csv(\"1th_ensemble.csv\", index=False)"],"execution_count":213,"outputs":[]},{"cell_type":"code","metadata":{"id":"8ix246Hnjyqm","executionInfo":{"status":"ok","timestamp":1603538697782,"user_tz":-330,"elapsed":1193,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"cf3a94d8-237a-4e33-e085-695fd478abf3","colab":{"base_uri":"https://localhost:8080/","height":419}},"source":["sol_fl.round(5)"],"execution_count":215,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10.13123</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>68.90114</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>69.06816</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>30.22132</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>12.28369</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>68715</th>\n","      <td>102.79967</td>\n","    </tr>\n","    <tr>\n","      <th>68716</th>\n","      <td>122.85984</td>\n","    </tr>\n","    <tr>\n","      <th>68717</th>\n","      <td>5015.34694</td>\n","    </tr>\n","    <tr>\n","      <th>68718</th>\n","      <td>79.39762</td>\n","    </tr>\n","    <tr>\n","      <th>68719</th>\n","      <td>705.35612</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>68720 rows × 1 columns</p>\n","</div>"],"text/plain":["       TARGET(PRICE_IN_LACS)\n","0                   10.13123\n","1                   68.90114\n","2                   69.06816\n","3                   30.22132\n","4                   12.28369\n","...                      ...\n","68715              102.79967\n","68716              122.85984\n","68717             5015.34694\n","68718               79.39762\n","68719              705.35612\n","\n","[68720 rows x 1 columns]"]},"metadata":{"tags":[]},"execution_count":215}]},{"cell_type":"code","metadata":{"id":"geNuFZwfjyjp"},"source":["sol_fl.round(1).to_csv(\"round_1_lin_ensemble.csv\", index=False)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Xr6WlyqyaWnd"},"source":["# Ridge"]},{"cell_type":"code","metadata":{"id":"L88egagBaV8k","executionInfo":{"status":"ok","timestamp":1603538710424,"user_tz":-330,"elapsed":1508,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"3b7b4b04-5f15-4415-d508-a3d73488ecdb","colab":{"base_uri":"https://localhost:8080/","height":408}},"source":["from sklearn.linear_model import Ridge\n","clf = Ridge(tol=1e-8, max_iter=5000, alpha=50)\n","\n","ens_oofs, ens_preds = clf_run(clf, train_new, test_new, features)\n"],"execution_count":216,"outputs":[{"output_type":"stream","text":["\n","------------- Fold 1 -------------\n","\n","AV metric score for validation set is 0.25892893435324893\n","\n","------------- Fold 2 -------------\n","\n","AV metric score for validation set is 0.2687283402320222\n","\n","------------- Fold 3 -------------\n","\n","AV metric score for validation set is 0.26225700318101597\n","\n","------------- Fold 4 -------------\n","\n","AV metric score for validation set is 0.2638419679297593\n","\n","------------- Fold 5 -------------\n","\n","AV metric score for validation set is 0.28578259970909786\n","\n","\n","AV metric for oofs is 0.2680749317269472\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"28oBSVGLdqxd","executionInfo":{"status":"ok","timestamp":1603538736336,"user_tz":-330,"elapsed":1264,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}}},"source":["pred = np.expm1(ens_preds)\n","sol_fl = pd.DataFrame()\n","sol_fl[TARGET_COL] = pred\n","sol_fl.to_csv(\"1st_ridge_ensemble.csv\", index=False)"],"execution_count":217,"outputs":[]},{"cell_type":"code","metadata":{"id":"kINcr5wdkrtI","executionInfo":{"status":"ok","timestamp":1603508949118,"user_tz":-330,"elapsed":3625,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"41d40b47-c704-426b-f313-94715bc8b188","colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["sol = pd.read_csv('/content/drive/My Drive/Machine_Hack/House Price Prediction Challenge/ridge_ensemble.csv')\n","sol.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9.796831</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>71.852500</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>66.988594</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>27.562719</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>13.200583</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   TARGET(PRICE_IN_LACS)\n","0               9.796831\n","1              71.852500\n","2              66.988594\n","3              27.562719\n","4              13.200583"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"id":"iR59-IxflkEr","executionInfo":{"status":"ok","timestamp":1603509418767,"user_tz":-330,"elapsed":1837,"user":{"displayName":"Shivkp","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjcodmiuhAdyhEvKfWS1UmmglMwYOb-cvywpkwh=s64","userId":"09987478781217827388"}},"outputId":"1c370151-2aea-4813-9594-1b186b546ecf","colab":{"base_uri":"https://localhost:8080/","height":111}},"source":["sol.round(2).head(2)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>TARGET(PRICE_IN_LACS)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9.80</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>71.85</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   TARGET(PRICE_IN_LACS)\n","0                   9.80\n","1                  71.85"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"8v4NaTkZlXlX"},"source":["sol.round(1).to_csv(\"round_ensemble_1.csv\", index=False)"],"execution_count":null,"outputs":[]}]}